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Record W3185672849 · doi:10.18174/526843

Market potential and investment opportunities of high-tech greenhouse vegetable production in the USA : An exploratory study for Midwest and East Coast regions and the state of California

2020· report· en· W3185672849 on OpenAlexaffabout
M.N.A. Ruijs, J. Benninga

Bibliographic record

Venuenot available
Typereport
Languageen
FieldAgricultural and Biological Sciences
TopicFlowering Plant Growth and Cultivation
Canadian institutionsImpact
FundersRijksdienst voor Ondernemend Nederland
KeywordsInvestment (military)GreenhouseProduction (economics)Agricultural economicsConsumption (sociology)East coastBusinessExploratory analysisExploratory researchState (computer science)High techGeographyEconomyEconomicsPolitical scienceHorticultureArchaeology

Abstract

fetched live from OpenAlex

An exploratory study was conducted into the market potential of high-tech greenhouses in the Midwest and East Coast regions and the state of California in the United States (USA) and the investment opportunities for Dutch horticultural supply companies. Insight is given into the production, import and consumption of (fresh) fruit vegetable products in the US. It is estimated how large the high-tech greenhouse horticulture in the USA (in terms of acreage and investments) can theoretically have if the imports from Mexico and Canada are replaced by their own production. It is then indicated indicatively which investment opportunities this can provide to Dutch suppliers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.739

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.078
GPT teacher head0.235
Teacher spread0.158 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2020
Admission routes2
Has abstractyes

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